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Immunological Bioinformatics

Group leader: Ole Lund
Members: Andreas Holm Mattsson, Anne Blicher, Athanasios Arvanitidis, Cecilie Liv Bager, Christian Skjødt Hansen, Dilek Inekci, Jacob Hull Kristensen, Jamie Neubert Pedersen, Jens Vindahl Kringelum, Jesper Neergaard, Joachim Høg Mortensen, Johanne Ahrenfeldt, Jose Luis Bellod Cisneros, Julia Villarroel, Juliet Wairimu Frederiksen, Katrine Dragsbæk Madsen, Lene Holberg Blicher, Martin Rykær, Martin Christen Frølund Thomsen, Mette Voldby Larsen, Mikael Engmark, Morten Nielsen, Nicklas Skjoldager, Paolo Marcatili, Per Hägglund, Susanne Blume, Thomas Trolle, Vanessa Isabell Jurtz, Zacharias Brimnes Visby Damholt
Guest members: Ea Zankari, Lasse Bergmark, Marlene Hansen, Patrick Munk, Rolf Kaas Mortensen, Shinny Leekitcharoenphon, Sidsel Nag, Simon Welner

Catalog of previous student projects.


immunological bioinformatics Textbook on:
Immunological Bioinformatics
by Ole Lund, Morten Nielsen, Claus Lundegaard, Can Keşmir and Søren Brunak

MIT Press
September 2005
ISBN 0-262-12280-4
7 x 9, 312 pp., 93 illus.
24-page color insert
$50.00


Blurb and Ordering at MIT Press       Link to Amazon


CBS researchers winner of machine learning in immunology (MLI) competition

The immune system normally does a good job of keeping us free from diseases, but sometimes it fails. One approach towards understanding why this happens is to produce advanced simulation models of the immune system and to understand the relationship between hosts and patogens in this manner. Depending on the complexity of these models and the input given, they can be used to simulate what happens when a host gets infected by a pathogen, thereby predicting the co-evolvement of pathogens and immune systems. One aim of the modeling is to identify parts of proteins known as epitopes which are recognized by the immune system, thereby inducing a protective response. This knowledge is very valuable in the development of better vaccines and provides very important insights into the nature of cancer, allergy and autoimmune diseases.

The Immunological Bioinformatics Group at CBS is developing new technologies for epitope discovery that can aid in the search for new vaccines and therapies for HIV, malaria, and tuberculosis, as well as for diseases such as influenza and pox, which may evolve to be a threat naturally or intentional through bioterrorism. The group has built a simulation model of the human immune system and has constructed a database with all human pathogens. Using this database and a database of the human genome the group is working on using the prediction methods to simulate the co-evolvement of pathogens and immune systems, and in particular to identify epitopes from the different arms of immune systems. In most of the projects the predicted epitopes are being validated through experimental collaborations with partners doing wet-lab research.

The group have developed methods for the three main types of epitopes: B cell epitopes which are used to recognize microorganisms outside cells; Helper T lymphocyte (HTL) epitopes which are used to activate cells that have taken up foreign substances; and cytotoxic T lymphocyte (CTL) epitopes, which are used to detect and kill infected cells. Current projects include:
  • Development of accurate methods for predicting peptide binding to MHC, Class I and Class II HLA molecules
  • Prediction of conformational and linear B cell epitopes
  • Optimization of plasmids containing multiple epitopes,
  • Proteasomal cleavage site predictions and prediction of CTL response
  • Epitope/pathogen database construction
  • Prediction of pathogenecity
  • Prediction of protein structure
The group is involved in the following EU and NIH, and Danish strategic research council projects projects:
A number of online servers has been developed by the immunological bioinformatics group.

Immunological feature predictors
BepiPred
Linear B-cell epitopes
DiscoTope
Discontinuous B-cell epitopes
NetChop
Proteasomal cleavages (MHC ligands)
NetCTL
Integrated class I antigen presentation
NetMHC
Binding of peptides to MHC class I alleles
NetMHCII
Binding of peptides to MHC class II alleles
NetMHCIIpan
Pan-specific binding of peptides to MHC class II HLA-DR alleles of known sequence
NetMHCpan
Pan-specific binding of peptides to MHC class I alleles of knownsequence
VDJsolver
Analysis of human immunoglobulin VDJ recombination

Some of these servers are also available via the Immune Epitope Database (IEDB)
www.iedb.org

Online tools
The immunological bioinformatics group has made the folowing online tools available:
EasyGibbs
Motif recognition in protein sequences by Gibbs sampler
EasyPred
Development of neural network and weight matrix prediction methods for protein sequences
MHCMotifViewer
Easy browsing and visualisation of MHC class I and II binding motifs
EpiPlot
Visualisation epitope positions relative to a reference strain
SigniSite
Identification of sites significantly correlated with phenotype in a multiple alignment with phenotype assignments


Online protein structure servers
The immunological bioinformatics group has been involved with developing methods for protein structure prediction.
CPHmodels
Prediction of protein tertiary structure from the sequence
NetSurfP
Prediction of solvent exposed sites in proteins

Teaching
The group is responsible for the following CBS courses:
Immunological Bioinformatics (masters/PhD level)
Algorithms in Bioinformatics - #27623 (masters/PhD level)
Biovidenskab (Bachelor level)

The group is also involved in the following courses:
Introduction to Bioinformatics, Protein Structure and Computational Biology - #27617, Introduction to Bioinformatics, turbo version - #27622, Biological Sequence Analysis - #27803 - INTERNET TRANSMISSION>, Bioinformatics for Human Biologists - course programme, winter 2010, Bioinformatik - It og Sundhed, BioBusiness & Innovation Program.

Project work in Immunological Bioinformatics
We advise students who want to make a bachelor thesis in the group to take an introductury course to bioinformatics such as Introduction to Bioinformatics, and a programming course such as Perl and Unix for Bioinformaticians - #27619. Before a master project we further advise students to follow the course Algorithms in Bioinformatics - #27623
Bioinformatics tools for project work

Selected Publications:
  • Systematic characterisation of cellular localisation and expression profiles of proteins containing MHC ligands. Juncker AS, Larsen MV, Weinhold N, Nielsen M, Brunak S, Lund O. PLoS One. 2009 4:e7448.
    PMID: 19826487
  • A generic method for assignment of reliability scores applied to solvent accessibility predictions. Petersen B, Petersen TN, Andersen P, Nielsen M, Lundegaard C. BMC Struct Biol. 2009 9:51.
    PMID: 19646261
  • Pan-specific MHC class I predictors: a benchmark of HLA class I pan-specific prediction methods. Zhang H, Lundegaard C, Nielsen M. Bioinformatics. 2009 25:83-9. Epub 2008 Nov 7.
    PMID: 18996943
  • Peptide binding to HLA class I molecules: homogenous, high-throughput screening, and affinity assays. Harndahl M, Justesen S, Lamberth K, Røder G, Nielsen M, Buus S. J Biomol Screen. 2009 14:173-80. Epub 2009 Feb 4.
    PMID: 19196700
  • Critical role of glycosylation in determining the length and structure of T cell epitopes. Szabó TG, Palotai R, Antal P, Tokatly I, Tóthfalusi L, Lund O, Nagy G, Falus A, Buzás EI. Immunome Res. 2009 5:4.
    PMID: 19778434
  • NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction. Nielsen M, Lund O. BMC Bioinformatics. 2009 10:296.
    PMID: 19765293
  • The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding. Zhang H, Lund O, Nielsen M. Bioinformatics. 2009 25:1293-9. Epub 2009 Mar 17.
    PMID: 19297351
  • High-affinity human leucocyte antigen class I binding variola-derived peptides induce CD4+ T cell responses more than 30 years post-vaccinia virus vaccination. Wang M, Tang ST, Lund O, Dziegiel MH, Buus S, Claesson MH. Clin Exp Immunol. 2009 155:441-6.
    PMID: 19220834
  • NetMHCpan, a method for MHC class I binding prediction beyond humans. Hoof I, Peters B, Sidney J, Pedersen LE, Sette A, Lund O, Buus S, Nielsen M. Immunogenetics. 2009 61:1-13. Epub 2008 Nov 12.
    PMID: 19002680
  • MHC-I-restricted epitopes conserved among variola and other related orthopoxviruses are recognized by T cells 30 years after vaccination. Tang ST, Wang M, Lamberth K, Harndahl M, Dziegiel MH, Claesson MH, Buus S, Lund O. Arch Virol. 2008;153:1833-44. Epub 2008 Sep 12.
    PMID: 18797815
  • The peptide-binding specificity of HLA-A*3001 demonstrates membership of the HLA-A3 supertype. Lamberth K, Røder G, Harndahl M, Nielsen M, Lundegaard C, Schafer-Nielsen C, Lund O, Buus S. Immunogenetics. 2008 60:633-43. Epub 2008 Sep 4.
    PMID: 18769915
  • MHC motif viewer.Rapin N, Hoof I, Lund O, Nielsen M. Immunogenetics. 2008 60:759-65. Epub 2008 Sep 3.
    PMID: 18766337
  • Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan. Nielsen M, Lundegaard C, Blicher T, Peters B, Sette A, Justesen S, Buus S, Lund O. PLoS Comput Biol. 2008 4:e1000107.
    PMID: 18604266
  • Immune epitope database analysis resource (IEDB-AR). Zhang Q, Wang P, Kim Y, Haste-Andersen P, Beaver J, Bourne PE, Bui HH, Buus S, Frankild S, Greenbaum J, Lund O, Lundegaard C, Nielsen M, Ponomarenko J, Sette A, Zhu Z, Peters B. Nucleic Acids Res. 2008 36(Web Server issue):W513-8. Epub 2008 May 31.
    PMID: 18515843
  • NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11. Lundegaard C, Lamberth K, Harndahl M, Buus S, Lund O, Nielsen M. Nucleic Acids Res. 2008 36(Web Server issue):W509-12. Epub 2008 May 7.
    PMID: 18463140
  • Humans with chimpanzee-like major histocompatibility complex-specificities control HIV-1 infection. Hoof I, Kesmir C, Lund O, Nielsen M. AIDS. 2008 22:1299-303.
    PMID:18580609
  • Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers. Lundegaard C, Lund O, Nielsen M. Bioinformatics. 2008 24:1397-8. Epub 2008 Apr 14.
    PMID: 18413329
  • Broadly immunogenic HLA class I supertype-restricted elite CTL epitopes recognized in a diverse population infected with different HIV-1 subtypes. Pérez CL, Larsen MV, Gustafsson R, Norström MM, Atlas A, Nixon DF, Nielsen M, Lund O, Karlsson AC. J Immunol. 2008 ;180:5092-100.
    PMID: 18354235
  • Amino acid similarity accounts for T cell cross-reactivity and for "holes" in the T cell repertoire. Frankild S, de Boer RJ, Lund O, Nielsen M, Kesmir C. PLoS One. 2008 3:e1831.
    PMID: 18350167
  • Structural insight into epitopes in the pregnancy-associated malaria protein VAR2CSA. Andersen P, Nielsen MA, Resende M, Rask TS, Dahlbäck M, Theander T, Lund O, Salanti A. PLoS Pathog. 2008 4:e42.
    PMID: 18282103
  • Modeling the adaptive immune system: predictions and simulations. Lundegaard C, Lund O, Kesmir C, Brunak S, Nielsen M. Bioinformatics. 2007 23:3265-75. Epub 2007 Nov 28.
    PMID: 18045832
  • Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction. Larsen MV, Lundegaard C, Lamberth K, Buus S, Lund O, Nielsen M. BMC Bioinformatics. 2007 8:424.
    PMID: 17973982
  • NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence. Nielsen M, Lundegaard C, Blicher T, Lamberth K, Harndahl M, Justesen S, Røder G, Peters B, Sette A, Lund O, Buus S. PLoS One. 2007 2:e796.
    PMID: 17726526
  • Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. Nielsen M, Lundegaard C, Lund O. BMC Bioinformatics. 2007 8:238.
    PMID: 17608956
  • CTL epitopes for influenza A including the H5N1 bird flu; genome-, pathogen-, and HLA-wide screening. Wang M, Lamberth K, Harndahl M, Røder G, Stryhn A, Larsen MV, Nielsen M, Lundegaard C, Tang ST, Dziegiel MH, Rosenkvist J, Pedersen AE, Buus S, Claesson MH, Lund O.Vaccine. 2007 25:2823-31. Epub 2006 Dec 29.
    PMID: 17254671
  • Towards a consensus on datasets and evaluation metrics for developing B-cell epitope prediction tools. Greenbaum JA, Andersen PH, Blythe M, Bui HH, Cachau RE, Crowe J, Davies M, Kolaskar AS, Lund O, Morrison S, Mumey B, Ofran Y, Pellequer JL, Pinilla C, Ponomarenko JV, Raghava GP, van Regenmortel MH, Roggen EL, Sette A, Schlessinger A, Sollner J, Zand M, Peters B.J Mol Recognit. 2007 20:75-82. Review.
    PMID: 17205610
  • Epitope mapping and topographic analysis of VAR2CSA DBL3X involved in P. falciparum placental sequestration. Dahlbäck M, Rask TS, Andersen PH, Nielsen MA, Ndam NT, Resende M, Turner L, Deloron P, Hviid L, Lund O, Pedersen AG, Theander TG, Salanti A.PLoS Pathog. 2006 2:e124.
    PMID: 17112315
  • The validity of predicted T-cell epitopes. Lundegaard C, Nielsen M, Lund O.Trends Biotechnol. 2006 24:537-8. Epub 2006 Oct 12.
    PMID: 17045685
  • Modelling the human immune system by combining bioinformatics and systems biology approaches. Rapin N, Kesmir C, Frankild S, Nielsen M, Lundegaard C, Brunak S, Lund O.J Biol Phys. 2006 32:335-53. Epub 2006 Oct 27.
    PMID: 19669470
  • Prediction of residues in discontinuous B-cell epitopes using protein 3D structures. Haste Andersen P, Nielsen M, Lund O.Protein Sci. 2006 Nov;15(11):2558-67. Epub 2006 Sep 25.Related articlesFree article
    PMID: 17001032
  • A community resource benchmarking predictions of peptide binding to MHC-I molecules. Peters B, Bui HH, Frankild S, Nielson M, Lundegaard C, Kostem E, Basch D, Lamberth K, Harndahl M, Fleri W, Wilson SS, Sidney J, Lund O, Buus S, Sette A.PLoS Comput Biol. 2006 2:e65. Epub 2006 Jun 9.Related articlesFree article
    PMID: 16789818
  • Ten years of bacterial genome sequencing: comparative-genomics-based discoveries. Binnewies TT, Motro Y, Hallin PF, Lund O, Dunn D, La T, Hampson DJ, Bellgard M, Wassenaar TM, Ussery DW.Funct Integr Genomics. 2006 6:165-85. Epub 2006 May 12. Review.
    PMID: 16773396
  • Improved method for predicting linear B-cell epitopes, Larsen JE, Lund O, Nielsen M, Immunome Res. 2:2, 2006
    PMID: 16635264
  • The design and implementation of the immune epitope database and analysis resource, Peters B, Sidney J, Bourne P, Bui HH, Buus S, Doh G, Fleri W, Kronenberg M, Kubo R, Lund O, Nemazee D, Ponomarenko JV, Sathiamurthy M, Schoenberger SP, Stewart S, Surko P, Way S, Wilson S, Sette A, Immunogenetics, 57, 326-36, 2005.
    PMID: 15895191
  • The immune epitope database and analysis resource: from vision to blueprint, Peters B, Sidney J, Bourne P, Bui HH, Buus S, Doh G, Fleri W, Kronenberg M, Kubo R, Lund O, Nemazee D, Ponomarenko JV, Sathiamurthy M, Schoenberger S, Stewart S, Surko P, Way S, Wilson S, Sette A, PLoS Biol, 3:e91, 2005.
    PMID15760272
  • An integrative approach to CTL epitope prediction: A combined algorithm integrating MHC class I binding, TAP transport, Larsen, Lundegaard C, Lamberth K, Buus S, Brunak S, Lund O, Nielsen M, Eur J Immunol, 2295-2303, 2005.
    PMID15997466
  • The role of the proteasome in generating cytotoxic T-cell epitopes: insights obtained from improved predictions of proteasomal cleavage, Nielsen M, Lundegaard C, Lund O, Kesmir C, Immunogenetics, 57, 33-41, 2005.
    PMID 15744535
  • SARS CTL vaccine candidates; HLA supertype-, genome-wide scanning and biochemical validation, C Sylvester-Hvid, M Nielsen, K Lamberth, G Roder, S Justesen, C Lundegaard, P Worning, H Thomadsen, O Lund, S Brunak, S Buus, Tissue Antigens, 63, 395-400, 2004.
    PMID: 15104671
  • Definition of supertypes for HLA molecules using clustering of specificity matrices, O Lund, M Nielsen,C Kesmir, AG Pedersen, C Lundegaard, P Worning, C Sylvester-Hvid, K Lamberth, G Roder, S Justesen, S Buus, S Brunak, Immunogenetics, 55, 797-810, 2004.
    PMID: 14963618
Full length list of CBS publications